Objective Achieving both a large field of view and high resolution remains a persistent and critical bottleneck in the field of optical imaging. Conventional imaging systems are constrained by a fundamental trade-off: expanding the field of view typically leads to a noticeable reduction in spatial resolution or requires the adoption of optical components that are bulkier, heavier, and more complex in structure, which limits their applicability in scenarios requiring portability or miniaturization. Existing solutions aimed at addressing this dilemma, such as those relying on mechanical translation stages, oscillating mirrors, or multi-lens arrays, suffer from inherent limitations. Mechanical translation stages are restricted by mechanical inertia, resulting in scanning frequencies as low as 10 Hz that severely hinder imaging efficiency; oscillating mirrors are prone to mirror surface deformation due to centrifugal forces at high speeds, with their maximum scanning frequency capped at around 100 Hz; while multi-lens array systems significantly increase overall system complexity, require highly efficient processing algorithms, and rely excessively on precise calibration procedures, with an equivalent scanning frequency of merely 50 Hz. To overcome these challenges, this thesis conducts in-depth research on large field of view stitching imaging technology based on galvanometer fast scanning. The primary objective is to fully leverage the high-speed scanning advantage of galvanometers (capable of kHz-level scanning frequencies, far exceeding traditional mechanical systems) to construct an efficient and reliable image acquisition system with an overlap compensation mechanism. Simultaneously, the traditional Harris algorithm, a classic corner detection method in computer vision, is improved to enhance its performance in feature extraction and matching for stitching tasks. Ultimately, this research develops a practical method for acquiring overlapping sub-images and seamlessly stitching them into a single high-resolution panoramic image, thereby resolving the trade-off between field of view and resolution.

Methods The research methodology is based on two core aspects: the construction of a scanning imaging system with an overlap compensation mechanism and the introduction of key improvements to the traditional Harris corner detection algorithm. The scanning imaging system was constructed using a high-precision galvanometer and a GigE industrial camera, enabling the rapid acquisition of a grid of sub-images. On the algorithmic front, the thesis proposes two main modifications. First, a priori non-extremely large value suppression shape is adopted. This method determines the optimal suppression shape (square, circle, or ellipse) by calculating gradient directions from downsampled images, allowing the algorithm to adapt to different image textures. Second, a center region shielding adaptive non-extremely large value suppression method based on the overlap rate is proposed. This technique detects the horizontal and vertical overlap ratios between images and strategically applies suppression only within the overlapping regions, effectively reducing interference from non-overlapping areas. These improvements enhance the accuracy of corner point screening and the computational efficiency of the algorithm. The overall stitching process follows a designed "group-of-four" iterative strategy and employs homography estimation and weighted linear blending for seamless image fusion.

Results and Discussions The effectiveness of the proposed method was rigorously verified through simulation and experimental studies. Simulation verification involved partitioning ten high-resolution 9000 pixel×12000 pixel source images into sub-arrays with preset overlap rates of 30%, 40%, 50%, and 60%. The results demonstrated that, compared with the traditional adaptive non-extremely large value suppression algorithm, the proposed method achieved notably faster computational speed. It also generally yielded a higher number of valid corner matches and a higher correct matching rate, with the advantage being particularly pronounced at lower overlap rates. Computational efficiency was improved by more than 60% under the different overlapping rates tested. The stitched image exhibited high quality, with PSNR values exceeding 40 dB and SSIM values close to 1, indicating excellent fidelity and minimal distortion. For experimental validation, a 6×11 array of 2592 pixel×1944 pixel images was first captured using the constructed galvanometer scanning system. Applying the improved algorithm to this dataset successfully generated a seamless panoramic image with a final resolution of 9553 pixel×19149 pixel. Compared to the traditional method, the complete stitching process achieved a 39.69% improvement in computational efficiency and a 10.31% increase in the feature matching correct rate, which directly contributed to significantly enhanced visual consistency in the final large-field-of-view composite. These results substantiate that the integration of high-speed galvanometer scanning with the enhanced Harris algorithm effectively addresses key limitations such as the low scanning speed of conventional mechanical systems and the computational inefficiency of traditional global feature detection, establishing a practical and effective approach for high-resolution imaging over a large field of view.

Conclusions This research successfully develops a large field of view stitching imaging technology based on galvanometer scanning. By constructing a rapid scanning system and improving the Harris algorithm through a priori suppression shape and overlap-rate-based center shielding adaptive non-extremely large value suppression, the study provides an effective solution to the classic trade-off between field of view and resolution. The technology demonstrates substantial improvements in processing speed and stitching accuracy, enabling the generation of ultra-high-resolution panoramic images. The results offer a new technical paradigm for applications ranging from astronomical observation and satellite remote sensing to pathology slice scanning and endoscopic imaging. Future work may involve integrating deep learning techniques to further optimize the algorithm's adaptability and efficiency in complex scenarios.